Tracking based text detection and recognition from videos in python

 

Tracking based text detection and recognition from videos in python

 

 PROJECT ID: PYTHON28

 

PROJECT NAME: Tracking based text detection and recognition from videos in python

 

 

PROJECT CATEGORY: MCA / BCA / BCCA / MCM / POLY / ENGINEERING

 

PROJECT ABSTRACT:

Content-based multimedia database indexing and retrieval tasks require automatic extraction of descriptive features that are relevant to the subject materials (images, video, etc.). The typical low-level features that are extracted in images and video include measures of color [1], texture [2], or shape [3]. Although these features can easily be obtained, they do not give a precise idea of the image content. Extracting more descriptive features and higher level entities, such as text [4] and human faces [5], has recently attracted signi3cant research interest. Text embedded in images and video, especially captions, provide brief and important content information, such as the name of players or speakers, the title, location, date of an event, etc. This text can be a keyword resource as powerful as the information provided by speech recognizers. Besides, text-based search has been successfully applied in many applications, while the robustness and computation cost of feature matching algorithms based on other high-level features is not eAcient enough to be applied to large databases.

There are two problems in obtaining eAcient and robust text detection using machine learning tools. One is how to avoid performing computational intensive classi3cation on the whole image, the other is how to reduce the variance of character size and gray scale in the feature space before training. In this paper, we address these problems by proposing a localization/veri3cation scheme that quickly extracts text blocks in images with a low rejection rate. This localization process allows us to further extract individual text lines and normalize the size of the text. We then perform precise veri3cation in a set of feature spaces that are invariant to gray-scale changes.

SOFTWARE REQUIREMENTS:

OS                                : Windows

Python IDE                  : Python 2.7.x and above

Language                             : Python Programming

Database                             : MYSQL

 

HARDWARE REQUIREMENTS:

 RAM                :  4GB and Higher

Processor          :  Intel i3 and above

Hard Disk         : 500GB Minimum

 

Setting up Software Environment

Python is a high level, interpreted, interactive and object-oriented scripting language.

Python is designed to be highly readable and has fewer syntactical constructions than other languages. Python is used in the development of this model. In this experiment, the following python libraries are used to develop the machine learning models:

• NLTK: It is a python package which works with human language data and provides an easy-to-use interface to different lexical resources like WordNet and text processing libraries. These lexical resources are used for classification, tokenization, stemming, tagging, parsing, and semantic reasoning [23].

• Pandas: It is a python package which acts as a data analysis tool and deals with data structures. Pandas carry out entire data analysis workflow in Python without having to switch to a more domain specific language like R [46].

• Tweepy: It is used in accessing the Twitter API by establishing the connection and to gather tweets from Twitter [24]. This module is used to stream live tweets directly from Twitter in real-time.

• Numpy: NumPy is the fundamental package for computing with Python. It is used to add support to multi-dimensional arrays and matrices, with a large collection of high-level mathematical functions [47].

• scikit-learn: It is a simple and efficient tool for data mining and data analysis [47]. • matplotlib python library which generates plots, histograms, power spectra, bar charts, etc.

In this work matplotlib.pyplot module is used to plot the metrics [47].

• Gensim It is used to automatically extract semantic topics from documents, as efficiently as possible. Gensim is designed to process raw, unstructured text data. The algorithms in Gensim, such as Word2Vec where it automatically discovers the semantic structure of phrase by examining statistical co-occurrence patterns within a corpus of training documents. These algorithms are unsupervised. Once these statistical patterns are found, any plain text documents can be succinctly expressed in the new, semantic representation and queried for topical similarity against other documents [48].

• Keras: Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research [49]

 

TABLE OF CONTENTS

·        Title Page      

·        Declaration

·        Certification Page

·        Dedication

·        Acknowledgements

·        Table of Contents

·        List of Tables

·        Abstract

 

CHAPTER SCHEME

CHAPTER ONE: INTRODUCTION

CHAPTER TWO: OBJECTIVES

CHAPTER THREE: PRELIMINARY SYSTEM ANALYSIS

·         Preliminary Investigation

·         Present System in Use

·         Flaws In Present System

·         Need Of New System

·         Feasibility Study

·         Project  Category

CHAPTER FOUR: SOFTWARE ENGINEERING AND PARADIGM APPLIED   

·         Modules

·         System / Module Chart

CHAPTER FIVE: SOFTWARE AND HARDWARE REQUIREMENT

CHAPTER SIX: DETAIL SYSTEM ANALYSIS

·         Data Flow Diagram

·         Number of modules and Process Logic

·         Data Structures  and Tables

·         Entity- Relationship Diagram

·         System Design

·         Form Design 

·         Source Code

·         Input Screen and Output Screen

CHAPTER SEVEN: TESTING AND VALIDATION CHECK

CHAPTER EIGHT: SYSTEM SECURITY MEASURES

CHAPTER NINE: IMPLEMENTATION, EVALUATION & MAINTENANCE

CHAPTER TEN: FUTURE SCOPE OF THE PROJECT

CHAPTER ELEVEN: SUGGESTION AND CONCLUSION

CHAPTER TWELE: BIBLIOGRAPHY& REFERENCES          

Other Information

 

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